Basics of Statistics in Python with pandas, sklearn, matplotlib, and seaborn Libraries (PYTHAI1)

Programming, Python

This training focuses on the basics of statistics in Python and the pandas, sklearn, matplotlib, and seaborn libraries. Participants will learn about data analysis and interpretation, fundamental principles of machine learning, and data analysis visualization. The training includes descriptive statistics, probability and distribution, relationships between variables, inferential statistics, and working with categorical variables.

Participants will gain the skills needed to analyze and correctly interpret data, understand the basic principles of machine learning, and communicate data analysis conclusions to colleagues, superiors, subordinates, and business partners using data visualization libraries.

Location, current course term

Contact us

Custom Customized Training (date, location, content, duration)

The course:

Hide detail
  • Descriptive statistics
    1. Mean, median, variance, standard deviation, quartiles
    2. Calculation of descriptive statistics with pandas
    3. Visualization of descriptive statistics using matplotlib and seaborn
  • Probability and distribution
    1. Basics of probability
    2. Discrete and continuous distribution: binomial, normal, exponential, t-student
    3. Calculation of probability and percentiles with Python
  • Relationships between variables
    1. Correlation: Pearson's, Spearman's
    2. Calculation of correlation with pandas and visualization using seaborn
    3. Regression analysis: linear regression, least squares method
    4. Implementation of linear regression with sklearn
    5. Inferential statistics
  • Hypothesis, null hypothesis, alternative hypothesis
    1. Type I and II errors
    2. Basic tests: t-test, ANOVA, chi-square
    3. Implementation of tests with Python using the scipy library
  • Inferential statistics
    1. Hypothesis, null hypothesis, alternative hypothesis
    2. Type I and II errors
    3. Basic tests: t-test, ANOVA, chi-square
    4. Implementation of tests with Python using the scipy library
  • Working with categorical variables
    1. Identification of categorical variables
    2. Conversion of categorical variables to numerical
    3. Implementation of conversion of categorical variables with pandas and sklearn
Assumed knowledge:
Basic knowledge of Python and the Pandas library, ideally at the level of PYTH1 training
Recommended previous course:
Python - Programming Basics (PYTH1)
Schedule:
2 days (9:00 AM - 5:00 PM )
Course price:
752.00 € ( 909.92 € incl. 21% VAT)
Language:

Vybrané zákaznické reference

Ministerstvo pro místní rozvoj, Veronika M.
Basics of Statistics in Python with pandas, sklearn, matplotlib, and seaborn Libraries ( PYTHAI1)
"Školitel, pan Petr Rozkošný, byl výborným lektorem, jeho přístup byl profesionální a zároveň přívětivý. Kurz přizpůsobil naším individuálním potřebám, zajímal se o využití nabytých znalostí v praxi a snažil se přizpůsobit výklad našemu využití. S agenturou, zázemím a vybavením jsem celkově velmi spokojená a ráda kurzy dále doporučím i se účastním dalších. Děkuji. "